Quantum Computing Is an Orchestration Problem: I Built a Multi-Provider Scheduler and Measured What the Textbooks Only Describe

Description and use

Quantum cloud services suffer from unpredictable queues and poor metrics. The Quantum Orchestrator automatically selects the best backend, measures wait and execution times, and fidelity, showing that queue dominates and backend choice affects accuracy.

Problem

Quantum computing services expose long, unpredictable queues and unreliable backend availability, making scheduling and fidelity selection difficult for users.

Outcome

Measured queue-to-execution ratio of 1822:1 on IBM hardware; autonomous backend selection improved fidelity from ~94% to ~97%; a two-basis VQE achieved chemical accuracy, separating shot noise from bias.

Workflow

• 1. Submit a quantum circuit to Quantum Orchestrator.
• 2. Orchestrator queries each backend adapter for availability and estimated queue.
• 3. It selects the backend with the shortest expected wait and highest expected fidelity.
• 4. The job is submitted; the orchestrator waits, respecting a dynamic fallback threshold and absolute timeout.
• 5. Upon completion, the orchestrator records queue time, execution time, and fidelity.
• 6. Results are reported to the user, including any fallback to simulators if real hardware is unavailable.

Evidence

On the same IBM quantum processor, on the same afternoon, the same two-qubit circuit took 2 seconds to run and 61 minutes to reach the front of the queue — a queue-to-execution ratio of 1822:1.

Source

Autonomous backend selection moves not only wait time but fidelity (~94% vs 97% on the same circuit, different processors).

Source